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init model

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  1. README.md +72 -0
  2. ese_vovnet39b_int.onnx +3 -0
  3. eval_onnx.py +164 -0
  4. requirements.txt +8 -0
README.md ADDED
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+ ---
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+ license: apache-2.0
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+ datasets:
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+ - imagenet-1k
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+ metrics:
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+ - accuracy
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+ tags:
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+ - RyzenAI
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+ - vision
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+ - classification
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+ - pytorch
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+ ---
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+
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+ # ESE_VoVNet39b
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+ Quantized ESE_VoVNet39b model that could be supported by [AMD Ryzen AI](https://ryzenai.docs.amd.com/en/latest/).
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+
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+
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+ ## Model description
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+ VoVNet was first introduced in the paper [An Energy and GPU-Computation Efficient Backbone Network for Real-Time Object Detection](https://arxiv.org/abs/1904.09730). Pretrained on ImageNet-1k in timm by Ross Wightman using RandAugment RA recipe.
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+
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+ The model implementation is from [timm](https://huggingface.co/timm/ese_vovnet39b.ra_in1k).
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+
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+
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+ ## How to use
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+
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+ ### Installation
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+
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+ Follow [Ryzen AI Installation](https://ryzenai.docs.amd.com/en/latest/inst.html) to prepare the environment for Ryzen AI.
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+ Run the following script to install pre-requisites for this model.
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+
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+ ```bash
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+ pip install -r requirements.txt
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+ ```
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+
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+ ### Data Preparation
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+
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+ Follow [ImageNet](https://huggingface.co/datasets/imagenet-1k) to prepare dataset.
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+
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+ ### Model Evaluation
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+
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+ ```python
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+ python eval_onnx.py --onnx_model ese_vovnet39b_int.onnx --ipu --provider_config Path\To\vaip_config.json --data_dir /Path/To/Your/Dataset
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+ ```
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+
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+ ### Performance
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+
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+ |Metric |Accuracy on IPU|
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+ | :----: | :----: |
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+ |Top1/Top5| 78.96% / 94.53%|
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+
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+
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+ ```bibtex
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+ @misc{rw2019timm,
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+ author = {Ross Wightman},
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+ title = {PyTorch Image Models},
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+ year = {2019},
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+ publisher = {GitHub},
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+ journal = {GitHub repository},
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+ doi = {10.5281/zenodo.4414861},
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+ howpublished = {\url{https://github.com/huggingface/pytorch-image-models}}
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+ }
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+ ```
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+
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+ ```bibtex
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+ @inproceedings{lee2019energy,
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+ title = {An Energy and GPU-Computation Efficient Backbone Network for Real-Time Object Detection},
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+ author = {Lee, Youngwan and Hwang, Joong-won and Lee, Sangrok and Bae, Yuseok and Park, Jongyoul},
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+ booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops},
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+ year = {2019}
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+ }
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+
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+ ```
ese_vovnet39b_int.onnx ADDED
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+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:e54c608973ec2515497ebac87955f35900334235c6c235b2db73e0d154cbe8df
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+ size 102032170
eval_onnx.py ADDED
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+ #!/usr/bin/env python
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+
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+ from typing import Tuple
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+
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+ import argparse
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+ import onnxruntime
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+ import os
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+ import sys
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+ import time
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+ import torch
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+ import torchvision.datasets as datasets
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+ import torchvision.transforms as transforms
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+
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+ from torch.utils.data import DataLoader
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+ from tqdm import tqdm
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+
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+ parser = argparse.ArgumentParser()
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+ parser.add_argument(
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+ "--onnx_model", default="model.onnx", help="Input onnx model")
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+ parser.add_argument(
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+ "--data_dir",
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+ default="/workspace/dataset/imagenet",
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+ help="Directory of dataset")
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+ parser.add_argument(
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+ "--batch_size", default=1, type=int, help="Evaluation batch size")
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+ parser.add_argument(
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+ "--ipu",
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+ action="store_true",
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+ help="Use IPU for inference.",
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+ )
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+ parser.add_argument(
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+ "--provider_config",
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+ type=str,
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+ default="vaip_config.json",
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+ help="Path of the config file for seting provider_options.",
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+ )
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+ args = parser.parse_args()
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+
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+ class AverageMeter(object):
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+ """Computes and stores the average and current value"""
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+
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+ def __init__(self, name, fmt=':f'):
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+ self.name = name
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+ self.fmt = fmt
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+ self.reset()
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+
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+ def reset(self):
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+ self.val = 0
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+ self.avg = 0
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+ self.sum = 0
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+ self.count = 0
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+
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+ def update(self, val, n=1):
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+ self.val = val
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+ self.sum += val * n
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+ self.count += n
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+ self.avg = self.sum / self.count
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+
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+ def __str__(self):
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+ fmtstr = '{name} {val' + self.fmt + '} ({avg' + self.fmt + '})'
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+ return fmtstr.format(**self.__dict__)
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+
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+ def accuracy(output: torch.Tensor,
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+ target: torch.Tensor,
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+ topk: Tuple[int] = (1,)) -> Tuple[float]:
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+ """Computes the accuracy over the k top predictions for the specified values of k.
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+ Args:
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+ output: Prediction of the model.
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+ target: Ground truth labels.
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+ topk: Topk accuracy to compute.
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+
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+ Returns:
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+ Accuracy results according to 'topk'.
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+ """
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+
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+ with torch.no_grad():
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+ maxk = max(topk)
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+ batch_size = target.size(0)
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+
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+ _, pred = output.topk(maxk, 1, True, True)
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+ pred = pred.t()
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+ correct = pred.eq(target.view(1, -1).expand_as(pred))
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+
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+ res = []
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+ for k in topk:
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+ correct_k = correct[:k].contiguous().view(-1).float().sum(0, keepdim=True)
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+ res.append(correct_k.mul_(100.0 / batch_size))
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+ return res
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+
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+ def prepare_data_loader(data_dir: str,
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+ batch_size: int = 100,
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+ workers: int = 8) -> torch.utils.data.DataLoader:
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+ """Returns a validation data loader of ImageNet by given `data_dir`.
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+
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+ Args:
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+ data_dir: Directory where images stores. There must be a subdirectory named
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+ 'validation' that stores the validation set of ImageNet.
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+ batch_size: Batch size of data loader.
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+ workers: How many subprocesses to use for data loading.
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+
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+ Returns:
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+ An object of torch.utils.data.DataLoader.
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+ """
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+
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+ valdir = os.path.join(data_dir, 'validation')
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+
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+ normalize = transforms.Normalize(
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+ mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
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+ val_dataset = datasets.ImageFolder(
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+ valdir,
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+ transforms.Compose([
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+ transforms.Resize(256),
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+ transforms.CenterCrop(224),
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+ transforms.ToTensor(),
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+ normalize,
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+ ]))
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+
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+ return torch.utils.data.DataLoader(
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+ val_dataset,
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+ batch_size=batch_size,
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+ shuffle=False,
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+ num_workers=workers,
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+ pin_memory=True)
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+
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+ def val_imagenet():
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+ """Validate ONNX model on ImageNet dataset."""
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+ print(f'Current onnx model: {args.onnx_model}')
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+
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+ if args.ipu:
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+ providers = ["VitisAIExecutionProvider"]
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+ provider_options = [{"config_file": args.provider_config}]
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+ else:
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+ providers = ['CUDAExecutionProvider', 'CPUExecutionProvider']
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+ provider_options = None
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+ ort_session = onnxruntime.InferenceSession(
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+ args.onnx_model, providers=providers, provider_options=provider_options)
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+
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+ val_loader = prepare_data_loader(args.data_dir, args.batch_size)
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+
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+ top1 = AverageMeter('Acc@1', ':6.2f')
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+ top5 = AverageMeter('Acc@5', ':6.2f')
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+
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+ start_time = time.time()
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+ val_loader = tqdm(val_loader, file=sys.stdout)
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+ with torch.no_grad():
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+ for batch_idx, (images, targets) in enumerate(val_loader):
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+ inputs, targets = images.numpy(), targets
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+ ort_inputs = {ort_session.get_inputs()[0].name: inputs}
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+
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+ outputs = ort_session.run(None, ort_inputs)
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+ outputs = torch.from_numpy(outputs[0])
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+
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+ acc1, acc5 = accuracy(outputs, targets, topk=(1, 5))
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+ top1.update(acc1, images.size(0))
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+ top5.update(acc5, images.size(0))
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+
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+ current_time = time.time()
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+ print('Test Top1 {:.2f}%\tTop5 {:.2f}%\tTime {:.2f}s\n'.format(
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+ float(top1.avg), float(top5.avg), (current_time - start_time)))
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+
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+ return top1.avg, top5.avg
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+
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+ if __name__ == '__main__':
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+ val_imagenet()
requirements.txt ADDED
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+ torch==1.12.0
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+ torchsummary==1.5.1
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+ torchvision==0.13.0
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+ Pillow==9.4.0
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+ onnx==1.14.0
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+ # onnxruntime-gpu==1.14.1
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+ timm==0.9.8
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+ tqdm==4.64.0